Learning from Topology: Cosmological Parameter Estimation from the Large-scale Structure

• URL: http://arxiv.org/abs/2308.02636v2
• Date: Wed, 04 Jun 2025 23:52:35 GMT
• Title: Learning from Topology: Cosmological Parameter Estimation from the Large-scale Structure
• Authors: Jacky H. T. Yip, Adam Rouhiainen, Gary Shiu,
• Abstract summary: We propose a neural network model to map persistence images to cosmological parameters.<n>Our model makes accurate and precise estimates, considerably outperforming conventional Bayesian inference approaches.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The topology of the large-scale structure of the universe contains valuable information on the underlying cosmological parameters. While persistent homology can extract this topological information, the optimal method for parameter estimation from the tool remains an open question. To address this, we propose a neural network model to map persistence images to cosmological parameters. Through a parameter recovery test, we demonstrate that our model makes accurate and precise estimates, considerably outperforming conventional Bayesian inference approaches.

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